Mobile devices are increasingly being used for entertainment, such as gaming and video streaming. The coexistence of these new services with the Internet of Things (IoT) and machine-to-machine communications means that wireless applications may quickly become starved for bandwidth. Millimeter wave and other radio frequency (RF) spectrum can provide much needed increases in throughput but pose a challenge of high-speed sampling required to sense the spectrum. This may be a particular challenge for relatively low-rate IoT applications, which are likely to benefit from opportunistic decentralized spectrum access. To achieve efficient usage of the spectrum, sensing techniques are needed which overcome the bottleneck of sampling at the Nyquist rate, which is generally too time and/or energy intensive, particularly for low-power wireless devices.
Two classes of solutions have been proposed to overcome this bottleneck. The first approach uses a sub-Nyquist sampling front end using analog-to-digital conversion techniques named xampling architectures. Xampling architectures preprocess a signal in the analog domain and then sample at a lower rate compared with what the Nyquist theorem dictates. The aim is to reduce the complexity and energy cost for the analog-to-digital converter hardware. However, prior xampling approaches increase complexity in the reconstruction of the underlying signal, and accurate spectrum sensing may not be guaranteed in lower signal-to-noise ratio (SNR) signals.
The second approach consists in selecting opportunistically, and in a cognitive fashion, a small section of spectrum at a time, relying on an analog front end able to switch between small sub-bands. This approach may be better able to cope with lower SNR signals. However, for sensing a broad spectrum, the second approach may take too much time to determine and use available spectrum before the environment has changed.